The Future & The Past

The most striking trend in the chart is the rapid population growth in Asia. From 1500, the continent’s population has grown from around 500 million to a projected 5561 million in 2150. The dynamic growth of the Asian population, especially in recent centuries, is a result of both high natural increase and advances in medicine and improved living conditions. This makes Asia the continent with the greatest market potential, but also poses challenges related to sustainable development and resource management. Similarly, Africa is experiencing significant population growth. From 1500, the population has grown from around 100 million to 2308 million in 2150. Africa, with its rapidly growing population, is becoming an increasingly important player on the global economic scene. The population increase brings with it the need to invest in infrastructure, education and healthcare to meet the needs of the growing community.

Latin America, Europe and North America are also experiencing population growth, albeit to a lesser extent. Europe, whose population grew from around 100 million in 1500 to a projected 517 million in 2150, has shown population stabilization in recent decades. An aging population and low natural increase in Europe pose challenges in maintaining economic balance and ensuring an adequate level of social care. North America, with a population growing from around 2 million in 1500 to 398 million in 2150, is also experiencing demographic stabilization. This region, like Europe, will need to adapt its policies to the changing demographic structure, with an emphasis on integrating immigrants and supporting the aging population

The Big Cultures

Central and South Asia are projected to see the most dramatic population increase, with numbers rising from 2.342 billion in 2022 to a projected 2.575 billion in 2050. This translates to a growth of 233 million people. East and Southeast Asia will also experience significant growth, with their populations increasing by 242 million to reach 2.317 billion. Sub-Saharan Africa is another region anticipating substantial growth, with projections showing a rise from 2.075 billion to 2.094 billion, indicating a strong natural increase. In contrast to the rapidly growing regions, Europe and North America are expected to remain fairly stable in terms of population. These regions might see a slight increase, going from a combined 1.152 billion to 1.125 billion. This demographic stability is likely a result of low birth rates and aging populations, posing challenges for these regions in terms of supporting their aging citizens and maintaining a dynamic workforce. North Africa and West Asia are expected to see their populations rise from 0.658 billion to 0.771 billion. Similarly, Latin America and the Caribbean are projected to grow from 0.549 billion to 0.749 billion. These regions will need to invest in infrastructure and social services to accommodate the growing needs of their populations.

Age Pyramid

The population pyramid displayed above offers a comprehensive snapshot of Poland’s demographic landscape in 2015, segmented by gender. This graphical representation delineates the population distribution across various age cohorts, highlighting the differences between males and females.

s <- svgstring(scaling = 1, width = 12, height = 15)

ageBins <- c(paste0(seq(0, 99, 5), "-", seq(4, 100, 5)), "100+")
dfAge[2:22, ]$Age <- ageBins

dfAge[nrow(dfAge) - 4:nrow(dfAge),] %>%
  mutate(
    Age = factor(Age, levels = ageBins, ordered = T),
    Women = sub(" ", "", sub(" ", "", Women)) %>% as.numeric,
    Men =  sub(" ", "", sub(" ", "", Men)) %>% as.numeric,
    `%age` = sub(",", ".", `%age`) %>% as.numeric
  ) %>%
  filter(Age != "Ogółem") %>%
  ggplot(aes(
    y = Age
  )) +
  geom_col(aes(x = Women), fill = "#d1000a") +
  geom_col(aes(x = -Men), fill = "#111d2b") +
  geom_text(aes(
    x = Women,
    label = format(Women, big.mark = " ", scientific = F),
    color = if_else(Women < 10^5*2, "black", "white"),
    hjust = if_else(Women < 10^5*2, 0, 1)
  ), show.legend = F) +
  geom_text(aes(
    x = -Men,
    label = format(Men, big.mark = " ", scientific = F),
    color = if_else(Men < 10^5*2, "black", "white"),
    hjust = if_else(Men < 10^5*2, 1, 0)
  ), show.legend = F) +
  scale_color_manual(values = c("black", "white")) +
  scale_x_continuous(
    breaks = seq(-1500000, 15000000, 500000),
    labels = format(abs(seq(-1500000, 15000000, 500000)), big.mark = " ", scientific = F)
  ) +
  labs(
    x = "",
    y = "",
    title = "Poland's demography (estimation, 2015)",
    subtitle = glue::glue(
      "
      gender-wise comparisons: 
      <span style = 'color: #111d2b;'><b>men</b></span>,
      <span style = 'color: #d1000a;'><b>women</b> </span>
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n demography")
  ) +
  theme(
    plot.subtitle = element_markdown()
  )

htmltools::HTML(s())

invisible(dev.off())

Proportion Curve

The population pyramid displayed above offers a comprehensive snapshot of Poland’s demographic landscape in 2015, segmented by gender. This graphical representation delineates the population distribution across various age cohorts, highlighting the differences between males and females.

s <- svgstring(scaling = 1, width = 12, height = 7)

dfAge[nrow(dfAge) - 4:nrow(dfAge),] %>%
  mutate(
    Age = factor(Age, levels = ageBins, ordered = T),
    Women = sub(" ", "", sub(" ", "", Women)) %>% as.numeric,
    Men =  sub(" ", "", sub(" ", "", Men)) %>% as.numeric,
    `%age` = sub(",", ".", `%age`) %>% as.numeric,
    rown = row_number(),
    Proportion = Men / Women
  ) %>%
  filter(Age != "Ogółem") %>%
  ggplot(aes(
    x = Proportion,
    y = reorder(Age, -rown)
  )) +
  geom_path(color = "#d1000a", size = 2, aes(group = "")) +
  coord_flip() +
  geom_text(aes(label = round(Proportion, 2)), angle = 90, nudge_x = .055, color = "#111d2b", fontface = "bold") +
  labs(
    x = "Number of males per 1 female",
    y = "",
    title = "Poland's demography (estimation, 2015)",
    subtitle = "How many males are there per female?",
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n demography")
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.y = element_blank(),
    panel.grid.major.x = element_line(linewidth = .1, linetype = 2, color = "#111d2b")
  )
htmltools::HTML(s())

invisible(dev.off())

Geospatial

The population of Poland is concentrated in a few key urban areas. As the capital, Warsaw stands out as the most densely populated area on the map, a natural consequence of its role as the country’s administrative, cultural, and economic center. Similarly, other large cities like Krakow, Lodz, Wroclaw, and Poznan also make their presence known on the map, indicating their significance as regional economic and educational hubs. The map also reveals clear distinctions between urban and rural areas. The northern and eastern regions of Poland, excluding major cities, are characterized by lower population density. These regions are dominated by smaller towns and villages, reflecting the greater role of agriculture and extractive industries in local economies. In contrast, the south and west, particularly the Silesia region, exhibit significant population density, mirroring the historical development of heavy industry and mining. The map also highlights the impact of transportation infrastructure on population distribution. Areas along major transportation corridors, such as highways and railways, often exhibit higher population densities. This is associated with better commuting opportunities and access to services, attracting residents and investments.

Early Years: Stability and Initial Decline (1990-2005)

In the early 1990s, Poland’s national team maintained a relatively stable position within the top 30 of the FIFA rankings. This period was characterized by consistent performances and a solid standing in international football. However, this stability began to wane in the mid-1990s. By 1997, the team experienced a noticeable drop, slipping out of the top 30 and continuing a downward trend that would culminate in their lowest point.

The Nadir: Hitting Rock Bottom (2006-2013)

The years 2006 to 2013 were particularly challenging for the Polish team. The chart highlights a dramatic plunge in rankings during this period, reaching the lowest point in 2013 when Poland was ranked 78th in the world. This phase was marked by underwhelming performances and a struggle to compete effectively on the international stage.

The Resurgence: Climbing to New Heights (2014-2017)

Despite the setbacks, the mid-2010s marked a period of remarkable recovery and resurgence for Poland. Starting from 2014, the team began a steady ascent in the rankings. This upward momentum reached its zenith in 2017, when Poland achieved its highest-ever FIFA ranking, climbing to 5th place. This remarkable turnaround can be attributed to a combination of strategic changes, improved player performances, and successful qualification campaigns.

Recent Years: Maintaining a Competitive Edge (2018-2023)

Following their peak in 2017, Poland’s ranking saw a slight decline but has remained within a competitive range, generally fluctuating between the 15th and 30th positions. This period indicates a phase of relative stability and competitiveness, reflecting the team’s ability to maintain a solid presence in international football.

Position over years

s <- svgstring(scaling = 1, width = 12, height = 6)

dfFifa %>%
  group_by(year) %>%
  mutate(mean = mean(position, na.rm = T)) %>%
  ggplot(aes(
    x = as.numeric(year),
    y = -mean
  )) +
  geom_line(color = "#F93943", size = 1.7) + 
  ### WORST
  annotate(
    "text",
    x = 2003.5,
    y = -68, 
    label = "The worst place in \n FIFA ranking \n (2013, 78th)",
    color = "#111d2b",
    fontface = 'bold',
    size = 5
  ) +
  geom_point(aes(y = -position), color = "#111d2b", size = 2, alpha = .5) +
  scale_x_continuous(breaks = seq(1990, max(dfFifa$year), na.rm = T, 2)) +
  scale_y_continuous(breaks = seq(0, -80, -5), labels =  seq(0, 80, 5)) +
  annotate(
    'curve',
    x = 2006.5,
    y = -72,
    yend = -78,
    xend = 2012.5,
    linewidth = 1,
    curvature = -0.2,
    arrow = arrow(length = unit(0.5, 'cm')),
    color = "#111d2b"
  ) +
  ### PEAK
  annotate(
    "text",
    x = 2012,
    y = -22, 
    label = "Poland's peak in \n FIFA ranking \n (2017, 5th)",
    color = "#111d2b",
    fontface = 'bold',
    size = 5
  ) +
  geom_point(aes(y = -position), color = "#111d2b", size = 2, alpha = .5) +
  annotate(
    'curve',
    x = 2014,
    y = -11.5,
    yend = -6.5,
    xend = 2016.5,
    linewidth = 1,
    curvature = -0.35,
    arrow = arrow(length = unit(0.5, 'cm')),
    color = "#111d2b"
  ) +
  labs(
    x = "Ranked year",
    y = "Position",
    title = "How is polish footbal team performing in the FIFA ranking?",
    subtitle = "year's average with each month's rank marked",
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())

invisible(dev.off())

Poland’s peak in 2017 is particularly noteworthy, as it signifies the culmination of efforts to rebuild and strengthen the team. Maintaining a competitive position in recent years suggests that Poland has established a more consistent framework for success, which could bode well for future international competitions.

Clear boxplot

s <- svgstring(scaling = 1, width = 12, height = 6)

 dfFifa %>%
  ggplot(aes(
    x = year,
    y = position
  )) +
  geom_boxplot() +
  labs(
    x = "Ranked year",
    y = "Position",
    title = "How is polish footbal team performing in the FIFA ranking?",
    subtitle = "year's average with each month's rank marked",
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )
 
htmltools::HTML(s())

invisible(dev.off())

Poland’s peak in 2017 is particularly noteworthy, as it signifies the culmination of efforts to rebuild and strengthen the team. Maintaining a competitive position in recent years suggests that Poland has established a more consistent framework for success, which could bode well for future international competitions.

Early Pioneers (1920s-1950s)

The journey of Polish football’s greatest strikers begins with the early pioneers of the 1920s and 1930s. Wawrzyniec Staliński, who scored 11 goals, and Józef Nawrot, with 16 goals, laid the foundational stones. These early stars set the stage for future generations, demonstrating the potential of Polish talent on the international stage.

Moving into the 1950s, we see the emergence of players like Gerard Cieślik, who netted 27 goals. His remarkable performance during this period helped elevate Poland’s status in international football, establishing a legacy that would inspire future strikers.

Golden Era (1960s-1980s)

The 1960s to the 1980s represent a golden era for Polish football, highlighted by prolific goal-scorers who dominated the field. Włodzimierz Lubański, with an impressive 48 goals, stands out as a towering figure of this era. His goal-scoring prowess and leadership on the field were instrumental in Poland’s successful campaigns during this period.

Other notable strikers from this era include Grzegorz Lato (45 goals) and Andrzej Szarmach (32 goals), both of whom played pivotal roles in Poland’s strong performances in international tournaments, including the World Cup. Their contributions cemented Poland’s reputation as a formidable footballing nation.

Transition and Consistency (1990s-2000s)

The 1990s and 2000s were marked by a blend of seasoned veterans and emerging talents. Players like Roman Kosecki (19 goals) and Andrzej Juskowiak (13 goals) provided consistency and experience, while younger stars such as Euzebiusz Smolarek (19 goals) began to make their mark.

This period also saw the rise of versatile midfielders like Jakub Błaszczykowski (21 goals), who, despite not being traditional strikers, contributed significantly to the team’s goal tally. Their performances ensured that Poland remained competitive on the international stage, setting the stage for future talents.

Modern Legends (2010s-Present)

The modern era of Polish football has been defined by the extraordinary talents of strikers like Robert Lewandowski, who has set a new benchmark with an astounding 82 goals. Lewandowski’s goal-scoring feats have not only brought numerous accolades to Poland but have also earned him a place among the world’s elite footballers. His consistency, skill, and leadership continue to inspire both teammates and fans.

Top Strikers

s <- svgstring(scaling = 1, width = 12, height = 9)

dfTeam %>%
  arrange(-debut) %>%
  mutate(
    id = row_number(),
    goals_buket = cut(goals, breaks = c(0, 19, 39, 1000), labels = c("[0, 19]", "[20, 39]", "40+"))
  ) %>%
  ggplot() + 
  geom_rect(aes(
    xmin = debut,
    xmax = off,
    ymin = id + .4,
    ymax = id - .4,
    fill = goals_buket
  ), show.legend = F) +
  geom_text(aes(
    x = debut - 1.5,
    y = if_else(id < 35, id, NA),
    label = paste0(player, " (", goals, ")")
  ), hjust = 1) + 
  geom_text(aes(
    x = off,
    y = id,
    label = paste0(off - debut, "y")
  ), hjust = 1, color = "white") + 
  geom_text(aes(
    x = off + 1.5,
    y = if_else(id >= 35, id, NA),
    label = paste0(player, " (", goals, ")")
  ), hjust = 0) + 
  scale_x_continuous(breaks = seq(min(dfTeam$debut, na.rm = T) - 2, max(dfTeam$off, na.rm = T), 4)) +
  theme(
    axis.line.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.subtitle = element_markdown()
  ) + 
  scale_fill_manual(
    values = c(
      "#111d2b",
      "#2f4f77",
      "#d1000a"
    )
  ) +
  labs(
    x = "Year played (from debut to off)",
    y = "",
    title = "The best stikers in the history of polish football",
    subtitle = glue::glue(
      "
      divided by a number (binned) of scored goals:
      <span style = 'color: #111d2b;'><b>[10, 19]</b></span>,
      <span style = 'color: #2f4f77;'><b>[20, 39]</b></span>,
      <span style = 'color: #d1000a;'><b>46+</b> </span>
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )


htmltools::HTML(s())

invisible(dev.off())

The historical overview of Poland’s greatest strikers, as illustrated in the chart, reflects a rich and evolving legacy of football talent. From early pioneers to modern legends, each era has produced remarkable goal-scorers who have contributed to the national team’s successes and reputation. As Poland continues to develop and nurture football talent, the achievements of these strikers will remain a source of inspiration and pride for future generations, ensuring that the legacy of Polish football excellence endures.

Detailed

s <- svgstring(scaling = 1, width = 12, height = 12)


dfTeam <- 
  dfTeam %>%
  mutate(
    ratio = round(goals / matches, 2)
  )

mostEfficient <- (
  dfTeam %>%
  top_n(., 5, ratio)
  )$player

dfTeam %>%
  mutate(
    isTop = player %in% mostEfficient,
    player = paste0(sub(" ", "\n", player), "\n(", goals, "/", matches, ")")
  ) %>%
  ggplot(aes(
    x = goals,
    y = matches,
    size = ratio
  )) + 
  geom_point(aes(
    color = isTop,
    size = ratio / 2
  ), alpha = .7, show.legend = F, vjust = "inward", hjust = "inward") +
  geom_text(aes(
    label = player,
    color = isTop
  ), alpha = .7, show.legend = F, fontface = 2, vjust = "inward", hjust = "inward") +
  scale_x_log10() + 
  scale_y_log10() +
  scale_size(range = c(3, 6)) +
  scale_color_manual(values = c("#111d2b", "#d1000a")) +
  theme(
    plot.subtitle = element_markdown()
  ) + 
  labs(
    x = "Goals Scored",
    y = "Matches Played",
    title = "The best stikers in the history of polish football",
    subtitle = glue::glue(
      "
      player efficiency comparison 
      <b>(goals scored / matches played)</b>
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())

invisible(dev.off())

The historical overview of Poland’s greatest strikers, as illustrated in the chart, reflects a rich and evolving legacy of football talent. From early pioneers to modern legends, each era has produced remarkable goal-scorers who have contributed to the national team’s successes and reputation. As Poland continues to develop and nurture football talent, the achievements of these strikers will remain a source of inspiration and pride for future generations, ensuring that the legacy of Polish football excellence endures.

Clear

s <- svgstring(scaling = 1, width = 12, height = 12)


dfTeam <- 
  dfTeam %>%
  mutate(
    ratio = round(goals / matches, 2)
  )

mostEfficient <- (
  dfTeam %>%
  top_n(., 5, ratio)
  )$player

dfTeam %>%
  mutate(
    isTop = player %in% mostEfficient,
    player = paste0(sub(" ", "\n", player), "\n(", goals, "/", matches, ")")
  ) %>%
  ggplot(aes(
    x = goals,
    y = matches,
    size = ratio
  )) + 
  geom_abline(intercept = 0, slope = 1, linetype = "dotted", color = "#d1000a") +
    annotate(
    "text",
    x = 35,
    y = 25, 
    label = "Lane indicates \n 1 goal per match \n performance",
    color = "#d1000a",
    fontface = 'bold',
    size = 4.5
  ) +
  geom_point(aes(
    color = isTop,
    size = ratio / 2
  ), alpha = .7, show.legend = F, vjust = "inward", hjust = "inward") +
  scale_x_log10() + 
  scale_y_log10() +
  scale_size(range = c(3, 6)) +
  scale_color_manual(values = c("#111d2b", "#d1000a")) +
  theme(
    plot.subtitle = element_markdown()
  ) + 
  labs(
    x = "Goals Scored",
    y = "Matches Played",
    title = "The best stikers in the history of polish football",
    subtitle = glue::glue(
      "
      player efficiency comparison 
      <b>(goals scored / matches played)</b>
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())

invisible(dev.off())

The historical overview of Poland’s greatest strikers, as illustrated in the chart, reflects a rich and evolving legacy of football talent. From early pioneers to modern legends, each era has produced remarkable goal-scorers who have contributed to the national team’s successes and reputation. As Poland continues to develop and nurture football talent, the achievements of these strikers will remain a source of inspiration and pride for future generations, ensuring that the legacy of Polish football excellence endures.

Scoring Goals

s <- svgstring(scaling = 1, width = 12, height = 18)

dfRival %>%
  group_by(Rival) %>%
  mutate(
    diff = GZ - GS,
    position = max(GZ, GS)
  ) %>%
  ggplot(aes(y = reorder(Rival, diff))) +
  geom_segment(aes(
    x = GZ,
    xend = GS,
    yend = reorder(Rival, diff)
  )) +
  geom_point(aes(x = GZ), color = "#d1000a", size = 5) +
  geom_point(aes(x = GS), color = "#0a0229", size = 4) +
  geom_text(aes(
    label = paste0(GZ, "/", GS),
    x = position + 4
  ), color = "#111d2b") +
  theme(
    plot.subtitle = element_markdown()
  ) + 
  labs(
    x = "Number of goals",
    y = "",
    title = "Who should we fear? The historical context.",
    subtitle = glue::glue(
      "
      Poland Team opponents by number of goals 
      <span style = 'color: #d1000a;'><b>scored</b></span> /
      <span style = 'color: #0a0229;'><b>lost</b></span>.
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())

invisible(dev.off())

Match Results

s <- svgstring(scaling = 1, width = 12, height = 18)

dfRival %>%
  group_by(Rival) %>%
  mutate(position = max(W, R, P)) %>%
  ggplot(aes(
    y = reorder(Rival, (W - P))
  )) +
  geom_segment(aes(
    x = W,
    xend = P,
    yend = reorder(Rival, (W - P))
  ), color = "#111d2b") +
  geom_point(aes(x = W), color = "#d1000a", size = 4) +
  geom_point(aes(x = R), color = "#2f5077", shape = 4, size = 4) +
  geom_point(aes(x = P), color = "#0a0229", size = 3.5) + 
  geom_text(aes(
    label = paste0(W, "/", P, "/", R),
    x = position + 1
  ), color = "#111d2b") +
  theme(
    plot.subtitle = element_markdown()
  ) + 
  labs(
    x = "Matches played",
    y = "",
    title = "Who should we fear? The historical context.",
    subtitle = glue::glue(
      "
      Poland Team opponents by matches 
      <span style = 'color: #d1000a;'><b>won</b></span> /
      <span style = 'color: #0a0229;'><b>lost</b></span> /
      <span style = 'color: #2f5077;'><b>draw</b></span>.
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())

invisible(dev.off())

Match Results Map

s <- svgstring(scaling = 1, width = 12, height = 8)

world <- ne_countries(scale = "medium", returnclass = "sf")
world_data <- world %>% 
  left_join(dfRival, by = c("name" = "Rival"))

world_data %>%
  mutate(Total = W - P) %>%
ggplot() +
  geom_sf(aes(fill = Total), color = "#ffffff") +
  scale_fill_gradient2(
    low = "#0a0229",
    mid = "#2f5077",
    high = "#d1000a",
    midpoint = 0,
    na.value = "#dddddd",
    name = "Matchs balance"
  ) +
  theme(
    plot.subtitle = element_markdown()
  ) + 
  labs(
    title = "Who should we fear? The historical context.",
    subtitle = glue::glue(
      "
      Poland Team opponents by matches mostly
      <span style = 'color: #d1000a;'><b>won</b></span> /
      <span style = 'color: #0a0229;'><b>lost</b></span> /
      <span style = 'color: #2f5077;'><b>draw</b></span>.
      "
    ),
    caption = paste0("Author: MK \n data source: wikipedia (", Sys.Date(), ") \n men's national football team")
  )

htmltools::HTML(s())